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    • 2. 发明申请
    • Electronic Stool Subtraction in Ct Colonography
    • 电子粪便减法在Ct结肠
    • US20080273781A1
    • 2008-11-06
    • US11816264
    • 2006-02-14
    • Armando ManducaMichael J. CarstonRobert J. WentzC. Daniel Johnson
    • Armando ManducaMichael J. CarstonRobert J. WentzC. Daniel Johnson
    • G06T7/20
    • G06T5/005G06T5/30G06T7/11G06T7/155G06T7/174G06T2207/10081G06T2207/20224G06T2207/30028
    • A method for processing CT colonography input image voxel data representative of 3-dimensional images of a colon having gas and stool tagged with stool tagging agent, to remove the stool from the images. The input image voxel data is generated by an imaging instrument having a characteristic point spread function representative of instrument blurring. The point spread function of the instrument can be empirically determined, and the image data processed as a function of the point spread function to accurately identify and remove the tagged stool. In one embodiment of the invention, portions of the image data representative of the tagged stool and colon tissue are dilated as a function of the point spread function. In another embodiment, portions of the image data representative of the tagged stool are convolved with the point spread function to determine the fractional amount of stool present in the image portions, and the tagged stool subtracted by reducing the intensities of the associated portions of the image by an amount proportional to the fractional amount of stool present.
    • 一种用于处理CT结肠造影输入图像体素数据的方法,其代表具有标记有粪便标记剂的气体和粪便的结肠的3维图像,以从图像中去除粪便。 输入图像体素数据由具有代表仪器模糊的特征点扩散函数的成像仪生成。 仪器的点扩散函数可以根据经验确定,并且图像数据作为点扩散函数的函数进行处理,以准确识别和移除标记的粪便。 在本发明的一个实施例中,代表标记的粪便和结肠组织的图像数据的部分作为点扩散函数的函数被扩张。 在另一个实施例中,代表标记的凳子的图像数据的部分与点扩散函数进行卷积,以确定图像部分中存在的粪便的分数,并且通过降低图像的相关部分的强度来减去标记的粪便 通过与存在的粪便的分数量成比例的量。
    • 3. 发明授权
    • Electronic stool subtraction in CT colonography
    • 电子粪便减法CT结肠造影
    • US08031921B2
    • 2011-10-04
    • US11816264
    • 2006-02-14
    • Armando ManducaMichael J. CarstonRobert J. WentzC. Daniel Johnson
    • Armando ManducaMichael J. CarstonRobert J. WentzC. Daniel Johnson
    • G06K9/00
    • G06T5/005G06T5/30G06T7/11G06T7/155G06T7/174G06T2207/10081G06T2207/20224G06T2207/30028
    • A method for processing CT colonography input image voxel data representative of 3-dimensional images of a colon having gas and stool tagged with stool tagging agent, to remove the stool from the images. The input image voxel data is generated by an imaging instrument having a characteristic point spread function representative of instrument blurring. The point spread function of the instrument can be empirically determined, and the image data processed as a function of the point spread function to accurately identify and remove the tagged stool. In one embodiment of the invention, portions of the image data representative of the tagged stool and colon tissue are dilated as a function of the point spread function. In another embodiment, portions of the image data representative of the tagged stool are convolved with the point spread function to determine the fractional amount of stool present in the image portions, and the tagged stool subtracted by reducing the intensities of the associated portions of the image by an amount proportional to the fractional amount of stool present.
    • 一种用于处理CT结肠造影输入图像体素数据的方法,其代表具有标记有粪便标记剂的气体和粪便的结肠的3维图像,以从图像中去除粪便。 输入图像体素数据由具有代表仪器模糊的特征点扩散函数的成像仪生成。 仪器的点扩散函数可以根据经验确定,并且图像数据作为点扩散函数的函数进行处理,以准确识别和移除标记的粪便。 在本发明的一个实施例中,代表标记的粪便和结肠组织的图像数据的部分作为点扩散函数的函数被扩张。 在另一个实施例中,代表标记的凳子的图像数据的部分与点扩散函数进行卷积,以确定图像部分中存在的粪便的分数,并且通过降低图像的相关部分的强度来减去标记的粪便 通过与存在的粪便的分数量成比例的量。
    • 7. 发明申请
    • ELECTRONIC STOOL SUBTRACTION USING QUADRATIC REGRESSION AND INTELLIGENT MORPHOLOGY
    • 使用四次回归和智能形态学的电子沉降
    • US20100128036A1
    • 2010-05-27
    • US12523484
    • 2008-01-22
    • C. Daniel JohnsonMichael J. CarstonArmando Manduca
    • C. Daniel JohnsonMichael J. CarstonArmando Manduca
    • G06T17/00G06K9/00
    • G06T7/0012G06T5/50G06T7/12G06T2207/10081G06T2207/30028
    • An improved method for processing image voxel data representative of 3-dimensional images of a colon to remove the effects of tagged stool. The method uses parabolic curve intensity-gradient models at a transition between two material types as a function of the fraction of the two materials for each of a plurality of two-material type classes, including a gas-tissue transition model, a gas-stool transition model and a stool-tissue transition model. The voxels are classified into one of a plurality of substance classes including tagged stool, gas, tissue and unknown classes. The unknown class voxels are processed to classify the unknown class voxels into one of the two-material type classes. The two-material type class voxels are processed to determine the fractions of materials in each voxel. The intensity of the two-material type class voxels is then adjusted as a function of the fraction of the materials in the voxels.
    • 用于处理表示结肠的3维图像的图像体素数据以改善标记的粪便的效果的改进方法。 该方法在两种材料类型之间的过渡处使用抛物线曲线强度梯度模型作为两种材料对于多种双材料类型中的每一种的分数的函数,包括气体组织转变模型,气体粪便 过渡模型和大便组织转移模型。 体素分为多种物质类别之一,包括标记的粪便,气体,组织和未知类别。 处理未知类体素以将未知类体素分类为双材质类之一。 处理双材料类型体素以确定每个体素中材料的分数。 然后将两材料类型体素的强度作为体素中材料分数的函数进行调整。
    • 9. 发明授权
    • Electronic stool subtraction using quadratic regression and intelligent morphology
    • 电子减法采用二次回归和智能形态学
    • US08564593B2
    • 2013-10-22
    • US12523484
    • 2008-01-22
    • C. Daniel JohnsonMichael J. CarstonArmando Manduca
    • C. Daniel JohnsonMichael J. CarstonArmando Manduca
    • G06T17/00G06T15/00
    • G06T7/0012G06T5/50G06T7/12G06T2207/10081G06T2207/30028
    • An improved method for processing image voxel data representative of 3-dimensional images of a colon to remove the effects of tagged stool. The method uses parabolic curve intensity-gradient models at a transition between two material types as a function of the fraction of the two materials for each of a plurality of two-material type classes, including a gas-tissue transition model, a gas-stool transition model and a stool-tissue transition model. The voxels are classified into one of a plurality of substance classes including tagged stool, gas, tissue and unknown classes. The unknown class voxels are processed to classify the unknown class voxels into one of the two-material type classes. The two-material type class voxels are processed to determine the fractions of materials in each voxel. The intensity of the two-material type class voxels is then adjusted as a function of the fraction of the materials in the voxels.
    • 用于处理表示结肠的3维图像的图像体素数据以改善标记的粪便的效果的改进方法。 该方法在两种材料类型之间的过渡处使用抛物线曲线强度梯度模型作为两种材料对于多种双材料类型中的每一种的分数的函数,包括气体组织转变模型,气体粪便 过渡模型和大便组织转移模型。 体素分为多种物质类别之一,包括标记的粪便,气体,组织和未知类别。 处理未知类体素以将未知类体素分类为双材质类之一。 处理双材料类型体素以确定每个体素中材料的分数。 然后将两材料类型体素的强度作为体素中材料分数的函数进行调整。